ATTENTION!! PLEASE RUN ALL THE CODE BELOW TO ENSURE ALL THE VARIABLES ARE IN THE GLOBAL ENVIRONMENT BEFORE LOOKING THROUGH THE ‘FINAL PROJECT’ FILE. SHORTCUT TO RUN ALL THE BELOW CODE: COMMAND + OPTION + R

Set-up

Load Tidyverse

library(tidyverse)
library(broom)
# load other packages AS NEEDED and ONLY IF YOU *REALLY* NEED THEM

#Load Data Load your data here:

fifa_data <- read.csv('data/fifa.raw.data.csv') #Import function used

#Exploratory data analysis Figure 1: Histogram of Overall Scores of players.

fifa_data %>% 
  ggplot(aes(x = Overall))+   
  geom_histogram(bins = 100)

Figure 2: Scatter plot between Price and Overall score

fifa_data %>% 
  filter(Price > 0) %>% 
  ggplot(data = .,aes(x = Overall, y= Price))+
  geom_point()+
  geom_vline(xintercept = 65, color = "Red")

Figure 2.1 Scatter plot between log_price and Overall score

#TRANSFORMATION OF PRICE
fifa_data <- fifa_data %>% 
  mutate(log_price = log(Price))
#Scatter plot after transformation
fifa_data %>% 
  filter(Price > 0) %>% 
  ggplot(data = .,aes(x = Overall, y= log_price))+
  geom_point()+
  geom_vline(xintercept = 65, color = "Red")+
  geom_smooth(method = "lm")

Figure 3: Linear Regression summary statistics

fifa_data %>% 
  filter(Price > 0) %>% 
  lm(formula = log_price ~ Overall, data = .) %>% 
  summary()

Call:
lm(formula = log_price ~ Overall, data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2226 -0.2069  0.0638  0.3184  1.2384 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.9933480  0.0348885   28.47   <2e-16 ***
Overall     0.1906596  0.0005239  363.90   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4859 on 17953 degrees of freedom
Multiple R-squared:  0.8806,    Adjusted R-squared:  0.8806 
F-statistic: 1.324e+05 on 1 and 17953 DF,  p-value: < 2.2e-16

Figure 4: Scatter plot showing the average price per playing position

fifa_data %>% 
  group_by(Position) %>% 
  summarize(avg_price_position = mean(Price)) %>% 
  ggplot(data = ., aes(x = Position, y = avg_price_position))+
  geom_point()

Figure 5: Outlier Identification

fifa_data %>% 
  slice_max(Price, n=30) %>% 
  arrange(desc(Price))

Figure 6: Analysis of relationship between salary and price of players

fifa_data %>% 
  ggplot(data = ., aes(x = Price, y = Salary))+
  geom_point(alpha = 0.25)+
  geom_vline(xintercept = 30000000, color = "red")+
  geom_smooth(method = "lm")

1 Analysis plan

##Player grouping

fifa_data <- fifa_data %>% 
  mutate(Grouped_Position = ifelse(fifa_data$Position == 'RF', 'STRIKER',
                        ifelse(fifa_data$Position == 'CAM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'CB', 'DEFENCE',
                        ifelse(fifa_data$Position == 'CDM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'CF', 'STRIKER',
                        ifelse(fifa_data$Position == 'CM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'GK', 'GOAL-KEEPER',
                        ifelse(fifa_data$Position == 'LAM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'LB', 'DEFENCE',
                        ifelse(fifa_data$Position == 'LCB', 'DEFENCE',
                        ifelse(fifa_data$Position == 'LDM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'LCM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'LF', 'WING',
                        ifelse(fifa_data$Position == 'LM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'LW', 'WING',
                        ifelse(fifa_data$Position == 'LWB', 'DEFENCE', 
                        ifelse(fifa_data$Position == 'RAM', 'MIDFIELD', 
                        ifelse(fifa_data$Position == 'RB', 'DEFENCE', 
                        ifelse(fifa_data$Position == 'RCB', 'DEFENCE',
                        ifelse(fifa_data$Position == 'RCM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'RDM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'RF', 'STRIKER',
                        ifelse(fifa_data$Position == 'RM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'RS', 'STRIKER',
                        ifelse(fifa_data$Position == 'RW', 'WING',
                        ifelse(fifa_data$Position == 'RWB', 'DEFENCE',
                        ifelse(fifa_data$Position == 'ST', 'STRIKER',
                        ifelse(fifa_data$Position == 'LS', 'STRIKER',
                        'other')))))))))))))))))))))))))))))
unique(fifa_data$Grouped_Position)
[1] "STRIKER"     "WING"        "GOAL-KEEPER" "MIDFIELD"    "DEFENCE"     "other"      

##Price analysis

fifa_data %>% # Create a boxplot to understand the outliers (Superstars)
  ggplot(data = ., mapping = aes(x= log_price)) +
  geom_boxplot()

#Calculate the range 
range(fifa_data$Price)
[1]         0 118500000
#Calculate the Quantiles to see the the distribution of prices under the curve
quantile(fifa_data$Price)
       0%       25%       50%       75%      100% 
        0    300000    675000   2000000 118500000 
# Filter data set by price, international reputation, and Potential to create the avg player data set
fifa_data_avg_player <- fifa_data %>% 
  filter(.data = ., Price < 9000000) 
#check the range in prices of the avg players
range(fifa_data_avg_player$Price)
[1]       0 8500000
#look at the distribution in prices of the avg player 
quantile(fifa_data_avg_player$Price) 
     0%     25%     50%     75%    100% 
      0  290000  625000 1400000 8500000 
fifa_superstars <- fifa_data %>% 
  filter(., Price > 9000000)  #number of superstar that we are not considering that are valued over $9,000,000 i.e SuperStars
fifa_data_avg_player %>% 
  group_by(Grouped_Position) %>% 
  count() 

##Superstar Averages

superstar_avg = fifa_superstars %>% 
  group_by(Grouped_Position) %>% 
  summarise(across(everything(), mean))

##Identifying stastically significant skills per position Playing position: STRIKER

#Finding out important attributes for Strikers players
fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "STRIKER") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .) %>% 
  summary()

Call:
lm(formula = log_price ~ Crossing + Finishing + HeadingAccuracy + 
    ShortPassing + Volleys + Dribbling + Curve + FKAccuracy + 
    LongPassing + BallControl + Acceleration + SprintSpeed + 
    Agility + Reactions + Balance + ShotPower + Jumping + Stamina + 
    Strength + LongShots + Aggression + Interceptions + Positioning + 
    Vision + Penalties + Composure + Marking + StandingTackle + 
    SlidingTackle + GKDiving + GKHandling + GKKicking + GKReflexes, 
    data = .)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.33350 -0.21454  0.00447  0.23684  1.76186 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)      1.516e+00  1.233e-01  12.288  < 2e-16 ***
Crossing        -5.232e-03  9.750e-04  -5.367 8.79e-08 ***
Finishing        4.499e-02  2.236e-03  20.125  < 2e-16 ***
HeadingAccuracy  1.528e-02  1.351e-03  11.307  < 2e-16 ***
ShortPassing     1.480e-02  1.696e-03   8.730  < 2e-16 ***
Volleys         -3.743e-03  1.211e-03  -3.092 0.002014 ** 
Dribbling        1.716e-02  2.006e-03   8.559  < 2e-16 ***
Curve            1.920e-03  1.009e-03   1.903 0.057177 .  
FKAccuracy      -1.215e-03  8.591e-04  -1.414 0.157390    
LongPassing      5.201e-04  1.121e-03   0.464 0.642765    
BallControl      2.759e-02  2.281e-03  12.096  < 2e-16 ***
Acceleration     1.267e-02  1.493e-03   8.487  < 2e-16 ***
SprintSpeed      1.381e-02  1.448e-03   9.535  < 2e-16 ***
Agility         -4.277e-03  1.147e-03  -3.729 0.000197 ***
Reactions        1.495e-02  1.673e-03   8.938  < 2e-16 ***
Balance         -2.611e-03  9.668e-04  -2.701 0.006971 ** 
ShotPower        1.852e-02  1.664e-03  11.127  < 2e-16 ***
Jumping         -5.901e-04  8.186e-04  -0.721 0.471067    
Stamina          1.238e-03  8.697e-04   1.423 0.154805    
Strength         3.943e-03  9.506e-04   4.148 3.47e-05 ***
LongShots        3.582e-03  1.571e-03   2.281 0.022660 *  
Aggression      -1.184e-03  6.453e-04  -1.834 0.066725 .  
Interceptions   -4.314e-03  9.628e-04  -4.480 7.80e-06 ***
Positioning      2.105e-02  1.848e-03  11.394  < 2e-16 ***
Vision           2.562e-03  1.268e-03   2.021 0.043423 *  
Penalties       -2.938e-03  1.145e-03  -2.566 0.010351 *  
Composure       -2.933e-03  1.399e-03  -2.097 0.036101 *  
Marking         -6.475e-06  7.528e-04  -0.009 0.993138    
StandingTackle   2.539e-03  1.388e-03   1.829 0.067477 .  
SlidingTackle   -9.909e-04  1.462e-03  -0.678 0.497953    
GKDiving        -1.662e-03  2.352e-03  -0.706 0.479950    
GKHandling      -7.020e-03  2.352e-03  -2.984 0.002870 ** 
GKKicking       -7.874e-03  2.347e-03  -3.355 0.000807 ***
GKReflexes      -2.971e-03  2.348e-03  -1.266 0.205784    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3517 on 2397 degrees of freedom
Multiple R-squared:  0.9104,    Adjusted R-squared:  0.9092 
F-statistic: 738.5 on 33 and 2397 DF,  p-value: < 2.2e-16

Playing position:WING

#Finding out important attributes for Wing players
fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "WING") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .) %>% 
  summary()

Call:
lm(formula = log_price ~ Crossing + Finishing + HeadingAccuracy + 
    ShortPassing + Volleys + Dribbling + Curve + FKAccuracy + 
    LongPassing + BallControl + Acceleration + SprintSpeed + 
    Agility + Reactions + Balance + ShotPower + Jumping + Stamina + 
    Strength + LongShots + Aggression + Interceptions + Positioning + 
    Vision + Penalties + Composure + Marking + StandingTackle + 
    SlidingTackle + GKDiving + GKHandling + GKKicking + GKReflexes, 
    data = .)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.38999 -0.21500 -0.00023  0.22006  1.46681 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)      1.1908575  0.2381362   5.001 7.38e-07 ***
Crossing         0.0002799  0.0024007   0.117  0.90723    
Finishing        0.0230509  0.0029409   7.838 1.91e-14 ***
HeadingAccuracy  0.0009490  0.0016562   0.573  0.56687    
ShortPassing     0.0286917  0.0039270   7.306 8.21e-13 ***
Volleys         -0.0030636  0.0020632  -1.485  0.13807    
Dribbling        0.0395802  0.0041069   9.638  < 2e-16 ***
Curve            0.0013492  0.0021479   0.628  0.53012    
FKAccuracy      -0.0007493  0.0016454  -0.455  0.64900    
LongPassing     -0.0028894  0.0024602  -1.174  0.24066    
BallControl      0.0380805  0.0046143   8.253 8.85e-16 ***
Acceleration     0.0157981  0.0036103   4.376 1.41e-05 ***
SprintSpeed      0.0101024  0.0032823   3.078  0.00217 ** 
Agility          0.0069129  0.0022584   3.061  0.00230 ** 
Reactions        0.0113778  0.0026221   4.339 1.66e-05 ***
Balance         -0.0038561  0.0018950  -2.035  0.04227 *  
ShotPower        0.0047774  0.0025076   1.905  0.05721 .  
Jumping         -0.0008994  0.0012185  -0.738  0.46071    
Stamina         -0.0015597  0.0015445  -1.010  0.31295    
Strength        -0.0018130  0.0014708  -1.233  0.21816    
LongShots        0.0023242  0.0023398   0.993  0.32092    
Aggression       0.0010249  0.0013276   0.772  0.44041    
Interceptions   -0.0014509  0.0016889  -0.859  0.39062    
Positioning      0.0168654  0.0030731   5.488 5.86e-08 ***
Vision           0.0088245  0.0027686   3.187  0.00151 ** 
Penalties        0.0003200  0.0018412   0.174  0.86210    
Composure       -0.0060656  0.0026096  -2.324  0.02042 *  
Marking         -0.0011175  0.0013160  -0.849  0.39610    
StandingTackle   0.0017219  0.0026171   0.658  0.51080    
SlidingTackle    0.0005470  0.0026116   0.209  0.83416    
GKDiving         0.0007695  0.0042722   0.180  0.85712    
GKHandling      -0.0019217  0.0043270  -0.444  0.65711    
GKKicking        0.0020192  0.0043011   0.469  0.63890    
GKReflexes      -0.0097271  0.0042956  -2.264  0.02388 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3325 on 640 degrees of freedom
Multiple R-squared:  0.9109,    Adjusted R-squared:  0.9063 
F-statistic: 198.3 on 33 and 640 DF,  p-value: < 2.2e-16

Playing position:MIDFIELD

#filtering for MIDFIELD players
fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "MIDFIELD") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .) %>% 
  summary()

Call:
lm(formula = log_price ~ Crossing + Finishing + HeadingAccuracy + 
    ShortPassing + Volleys + Dribbling + Curve + FKAccuracy + 
    LongPassing + BallControl + Acceleration + SprintSpeed + 
    Agility + Reactions + Balance + ShotPower + Jumping + Stamina + 
    Strength + LongShots + Aggression + Interceptions + Positioning + 
    Vision + Penalties + Composure + Marking + StandingTackle + 
    SlidingTackle + GKDiving + GKHandling + GKKicking + GKReflexes, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8560 -0.2904  0.0010  0.2977  4.6442 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)      1.3873342  0.1049614  13.218  < 2e-16 ***
Crossing         0.0005895  0.0009214   0.640   0.5223    
Finishing        0.0046988  0.0010002   4.698 2.69e-06 ***
HeadingAccuracy  0.0063712  0.0008132   7.835 5.47e-15 ***
ShortPassing     0.0382627  0.0020015  19.117  < 2e-16 ***
Volleys         -0.0064730  0.0008724  -7.420 1.33e-13 ***
Dribbling        0.0232587  0.0016916  13.749  < 2e-16 ***
Curve            0.0005979  0.0009166   0.652   0.5142    
FKAccuracy      -0.0051202  0.0007972  -6.422 1.44e-10 ***
LongPassing      0.0059561  0.0015142   3.933 8.46e-05 ***
BallControl      0.0495768  0.0020837  23.792  < 2e-16 ***
Acceleration     0.0135813  0.0012716  10.680  < 2e-16 ***
SprintSpeed      0.0105988  0.0011517   9.202  < 2e-16 ***
Agility         -0.0012798  0.0009962  -1.285   0.1989    
Reactions        0.0256071  0.0012652  20.240  < 2e-16 ***
Balance         -0.0045127  0.0008728  -5.171 2.41e-07 ***
ShotPower        0.0085062  0.0010750   7.913 2.96e-15 ***
Jumping         -0.0028407  0.0006091  -4.664 3.17e-06 ***
Stamina          0.0108043  0.0006998  15.439  < 2e-16 ***
Strength        -0.0021733  0.0007648  -2.842   0.0045 ** 
LongShots        0.0009019  0.0009844   0.916   0.3596    
Aggression       0.0003529  0.0006753   0.523   0.6012    
Interceptions   -0.0013366  0.0008714  -1.534   0.1251    
Positioning     -0.0043135  0.0010462  -4.123 3.79e-05 ***
Vision           0.0117389  0.0012936   9.074  < 2e-16 ***
Penalties       -0.0017317  0.0008624  -2.008   0.0447 *  
Composure        0.0046467  0.0010800   4.302 1.72e-05 ***
Marking          0.0011440  0.0006936   1.649   0.0991 .  
StandingTackle   0.0003005  0.0013176   0.228   0.8196    
SlidingTackle   -0.0004937  0.0011850  -0.417   0.6770    
GKDiving        -0.0017034  0.0019665  -0.866   0.3864    
GKHandling       0.0015909  0.0019997   0.796   0.4263    
GKKicking       -0.0010050  0.0019570  -0.514   0.6076    
GKReflexes      -0.0010012  0.0019664  -0.509   0.6107    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4748 on 6200 degrees of freedom
Multiple R-squared:  0.8411,    Adjusted R-squared:  0.8403 
F-statistic: 994.7 on 33 and 6200 DF,  p-value: < 2.2e-16

Playing position:DEFENCE

#filtering for DEFENCE players
fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "DEFENCE") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .) %>% 
  summary()

Call:
lm(formula = log_price ~ Crossing + Finishing + HeadingAccuracy + 
    ShortPassing + Volleys + Dribbling + Curve + FKAccuracy + 
    LongPassing + BallControl + Acceleration + SprintSpeed + 
    Agility + Reactions + Balance + ShotPower + Jumping + Stamina + 
    Strength + LongShots + Aggression + Interceptions + Positioning + 
    Vision + Penalties + Composure + Marking + StandingTackle + 
    SlidingTackle + GKDiving + GKHandling + GKKicking + GKReflexes, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6104 -0.2651  0.0265  0.3113  4.4003 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)      1.6563473  0.1137571  14.560  < 2e-16 ***
Crossing        -0.0115399  0.0008419 -13.706  < 2e-16 ***
Finishing        0.0023505  0.0009617   2.444 0.014548 *  
HeadingAccuracy  0.0144550  0.0011653  12.405  < 2e-16 ***
ShortPassing     0.0169758  0.0014669  11.573  < 2e-16 ***
Volleys         -0.0009286  0.0008861  -1.048 0.294688    
Dribbling        0.0049042  0.0010530   4.657 3.28e-06 ***
Curve            0.0014955  0.0008463   1.767 0.077275 .  
FKAccuracy      -0.0009494  0.0008187  -1.160 0.246248    
LongPassing     -0.0021353  0.0010710  -1.994 0.046228 *  
BallControl      0.0122519  0.0014595   8.394  < 2e-16 ***
Acceleration     0.0082434  0.0012618   6.533 7.04e-11 ***
SprintSpeed      0.0178852  0.0011496  15.558  < 2e-16 ***
Agility         -0.0020786  0.0009487  -2.191 0.028490 *  
Reactions        0.0181855  0.0015610  11.650  < 2e-16 ***
Balance         -0.0055301  0.0008954  -6.176 7.05e-10 ***
ShotPower        0.0035806  0.0008155   4.390 1.15e-05 ***
Jumping         -0.0009240  0.0007148  -1.293 0.196156    
Stamina          0.0093118  0.0008300  11.219  < 2e-16 ***
Strength         0.0022076  0.0010024   2.202 0.027693 *  
LongShots       -0.0038162  0.0009100  -4.194 2.79e-05 ***
Aggression       0.0025794  0.0008817   2.925 0.003456 ** 
Interceptions    0.0185252  0.0017078  10.847  < 2e-16 ***
Positioning     -0.0021971  0.0008757  -2.509 0.012139 *  
Vision          -0.0017855  0.0009522  -1.875 0.060831 .  
Penalties       -0.0021686  0.0008690  -2.496 0.012603 *  
Composure       -0.0039358  0.0011715  -3.360 0.000786 ***
Marking          0.0238039  0.0014564  16.344  < 2e-16 ***
StandingTackle   0.0455553  0.0025419  17.922  < 2e-16 ***
SlidingTackle    0.0186257  0.0022187   8.395  < 2e-16 ***
GKDiving         0.0010599  0.0021576   0.491 0.623277    
GKHandling      -0.0029921  0.0021736  -1.377 0.168702    
GKKicking       -0.0046790  0.0020928  -2.236 0.025407 *  
GKReflexes      -0.0016938  0.0021734  -0.779 0.435814    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4859 on 5473 degrees of freedom
Multiple R-squared:  0.8268,    Adjusted R-squared:  0.8258 
F-statistic: 791.7 on 33 and 5473 DF,  p-value: < 2.2e-16

Playing position:GOAL-KEEPER

#filtering for GOAL-KEEPER players
fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "GOAL-KEEPER") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .) %>% 
  summary()

Call:
lm(formula = log_price ~ Crossing + Finishing + HeadingAccuracy + 
    ShortPassing + Volleys + Dribbling + Curve + FKAccuracy + 
    LongPassing + BallControl + Acceleration + SprintSpeed + 
    Agility + Reactions + Balance + ShotPower + Jumping + Stamina + 
    Strength + LongShots + Aggression + Interceptions + Positioning + 
    Vision + Penalties + Composure + Marking + StandingTackle + 
    SlidingTackle + GKDiving + GKHandling + GKKicking + GKReflexes, 
    data = .)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0622 -0.2044  0.0853  0.3420  1.3112 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)      1.5942850  0.1680786   9.485  < 2e-16 ***
Crossing        -0.0069668  0.0041362  -1.684 0.092284 .  
Finishing        0.0008992  0.0051911   0.173 0.862493    
HeadingAccuracy  0.0030268  0.0038289   0.791 0.429318    
ShortPassing     0.0021277  0.0024034   0.885 0.376125    
Volleys         -0.0073119  0.0047757  -1.531 0.125921    
Dribbling        0.0001951  0.0039932   0.049 0.961044    
Curve            0.0112072  0.0038155   2.937 0.003351 ** 
FKAccuracy      -0.0072472  0.0035191  -2.059 0.039592 *  
LongPassing      0.0019851  0.0023130   0.858 0.390864    
BallControl     -0.0059622  0.0028935  -2.061 0.039483 *  
Acceleration     0.0081631  0.0023485   3.476 0.000521 ***
SprintSpeed      0.0008019  0.0023696   0.338 0.735084    
Agility         -0.0039030  0.0015197  -2.568 0.010296 *  
Reactions        0.0072068  0.0022905   3.146 0.001679 ** 
Balance         -0.0008057  0.0015447  -0.522 0.602046    
ShotPower        0.0035778  0.0021621   1.655 0.098127 .  
Jumping         -0.0030445  0.0015474  -1.967 0.049274 *  
Stamina          0.0038081  0.0020760   1.834 0.066752 .  
Strength         0.0001276  0.0013510   0.094 0.924755    
LongShots        0.0028381  0.0047439   0.598 0.549732    
Aggression      -0.0160938  0.0019230  -8.369  < 2e-16 ***
Interceptions   -0.0042204  0.0031320  -1.347 0.177986    
Positioning     -0.0157758  0.0048243  -3.270 0.001095 ** 
Vision           0.0023417  0.0012342   1.897 0.057928 .  
Penalties       -0.0069203  0.0023399  -2.958 0.003140 ** 
Composure       -0.0071827  0.0013829  -5.194 2.28e-07 ***
Marking         -0.0079515  0.0024770  -3.210 0.001349 ** 
StandingTackle   0.0134768  0.0049973   2.697 0.007063 ** 
SlidingTackle   -0.0009553  0.0048799  -0.196 0.844821    
GKDiving         0.0505630  0.0043600  11.597  < 2e-16 ***
GKHandling       0.0424519  0.0035073  12.104  < 2e-16 ***
GKKicking        0.0115107  0.0027443   4.194 2.86e-05 ***
GKReflexes       0.0773435  0.0041440  18.664  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5788 on 1880 degrees of freedom
Multiple R-squared:  0.8129,    Adjusted R-squared:  0.8097 
F-statistic: 247.6 on 33 and 1880 DF,  p-value: < 2.2e-16

##Player Identification ###FILTERING FOR STRIKERS - 2 Main SKILLS

fifa_data_avg_player %>%
  filter(Grouped_Position == "STRIKER") %>% 
  filter(Finishing >= 80.70 & HeadingAccuracy >= 73.98) %>% 
  arrange(desc(Price))

###FILTERING FOR WING - 2 Main SKILLS

fifa_data_avg_player %>%
  filter(Grouped_Position == "WING") %>% 
  filter(SprintSpeed >= 82.84 & Crossing >= 75.82) %>% 
  arrange(desc(Price))

###Filtering for midfield - 2 main SKILLS

fifa_data_avg_player %>% 
  filter(Grouped_Position == "MIDFIELD") %>%   
  filter(ShortPassing >= 79.67 & BallControl >= 80.84) %>% 
  arrange(desc(Price))

###FILTERING FOR DEFENCE - 2 Main SKILLS

fifa_data_avg_player %>% 
  filter(Grouped_Position == "DEFENCE") %>%   
  filter(Strength >= 77.35 & SlidingTackle >= 79.7 & StandingTackle >= 81) %>% 
  arrange(desc(Price))

###FILTERING FOR Goal-keeper - 2 Main SKILLS

fifa_data_avg_player %>% 
  filter(Grouped_Position == "GOAL-KEEPER") %>% 
  filter(GKDiving >= 82.34 & GKReflexes > 84.01 ) %>%
  arrange(desc(Price))

###Predicting Prices (Strikers) with Linear regression formula

lm_strikers = fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "STRIKER") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .)
  

strikers_filtered = fifa_data_avg_player %>%
  filter(Grouped_Position == "STRIKER" & Price > 0)

predict_p_df = as_data_frame(broom::augment(lm_strikers))

strikers_predited <- cbind(strikers_filtered, predict_p_df$.fitted)

strikers_predited %>%
  filter(`predict_p_df$.fitted`< log_price)
NA
NA

###Predicting Prices (Wing) with Linear regression formula

lm_wing = fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "WING") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .)
  

wing_filtered = fifa_data_avg_player %>%
  filter(Grouped_Position == "WING" & Price > 0)

predict_wing_df = as_data_frame(broom::augment(lm_wing))

wing_predited <- cbind(wing_filtered, predict_wing_df$.fitted)

wing_predited %>%
  filter(`predict_wing_df$.fitted`< log_price)

###Predicting Prices (MIDFIELD) with Linear regression formula

lm_midfield = fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "MIDFIELD") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .)
  

midfield_filtered = fifa_data_avg_player %>%
  filter(Grouped_Position == "MIDFIELD" & Price > 0)

predict_midfield_df = as_data_frame(broom::augment(lm_midfield))

midfield_predited <- cbind(midfield_filtered, predict_midfield_df$.fitted)

midfield_predited %>%
  filter(`predict_midfield_df$.fitted`< log_price)

###Predicting Prices (DEFENCE) with Linear regression formula

lm_defence = fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "DEFENCE") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .)
  

defence_filtered = fifa_data_avg_player %>%
  filter(Grouped_Position == "DEFENCE" & Price > 0)

predict_defence_df = as_data_frame(broom::augment(lm_defence))

defence_predited <- cbind(defence_filtered, predict_defence_df$.fitted)

defence_predited %>%
  filter(`predict_defence_df$.fitted`< log_price)

###Predicting Prices (GOAL-KEEPER) with Linear regression formula

lm_goalie = fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "GOAL-KEEPER") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .)
  

goalie_filtered = fifa_data_avg_player %>%
  filter(Grouped_Position == "GOAL-KEEPER" & Price > 0)

predict_goalie_df = as_data_frame(broom::augment(lm_goalie))

goalie_predited <- cbind(goalie_filtered, predict_goalie_df$.fitted)

goalie_predited %>%
  filter(`predict_goalie_df$.fitted`< log_price)

#Output Selecting Top Players for MIDFLIED Position, message=FALSE, warning=FALSE

midfield_predited %>% 
  filter(ShortPassing >= 79.67 & BallControl >= 80.84 & `predict_midfield_df$.fitted`> log_price) %>% 
  arrange(desc(Price))

Selecting Top Players for GOAL - KEEPER Position, message=FALSE, warning=FALSE

goalie_predited %>% 
  filter(GKDiving >= 82.34 & GKReflexes > 84.01  & `predict_goalie_df$.fitted`> log_price) %>% 
  arrange(desc(Price))

Selecting Top Players for DEFENCE Position, message=FALSE, warning=FALSE

defence_predited %>% 
  filter(Strength >= 77.35 & SlidingTackle >= 79.7 & StandingTackle >= 81  & `predict_defence_df$.fitted`> log_price) %>% 
  arrange(desc(Price))

Selecting Top Players for WING Position, message=FALSE, warning=FALSE

wing_predited %>% 
  filter(SprintSpeed >= 82.84 & Crossing >= 75.82  & `predict_wing_df$.fitted` > log_price) %>% 
  arrange(desc(Price))

Selecting Top Players for STRICKERS Position, message=FALSE, warning=FALSE

strikers_predited %>% 
  filter(Finishing >= 80.70 & HeadingAccuracy >= 73.98  & `predict_p_df$.fitted`> log_price) %>% 
  arrange(desc(Price))
---
title: "Team project: Checkpoint"
author: "Juan Pablo Jaramillo & Varun Vijayakumar - TEAM 2"
subtitle: "R for Data Science @ Hult University"
output:
  html_notebook:
    highlight: pygments
    number_sections: yes
    theme: readable
    toc: yes
    toc_float:
      collapsed: yes
---
ATTENTION!!
PLEASE RUN ALL THE CODE BELOW TO ENSURE ALL THE VARIABLES ARE IN THE GLOBAL ENVIRONMENT BEFORE LOOKING THROUGH THE 'FINAL PROJECT' FILE.
SHORTCUT TO RUN ALL THE BELOW CODE: COMMAND + OPTION + R

# Set-up {.unnumbered}
Load Tidyverse
```{r load packages, message=FALSE, warning=FALSE}
library(tidyverse)
library(broom)
# load other packages AS NEEDED and ONLY IF YOU *REALLY* NEED THEM
```

#Load Data
Load your data here:
```{r load data, message=FALSE, warning=FALSE}
fifa_data <- read.csv('data/fifa.raw.data.csv') #Import function used
```

#Exploratory data analysis
**Figure 1:** 
Histogram of Overall Scores of players.
```{r}
fifa_data %>% 
  ggplot(aes(x = Overall))+   
  geom_histogram(bins = 100)
```
**Figure 2:**
Scatter plot between Price and Overall score
```{r}
fifa_data %>% 
  filter(Price > 0) %>% 
  ggplot(data = .,aes(x = Overall, y= Price))+
  geom_point()+
  geom_vline(xintercept = 65, color = "Red")
```
**Figure 2.1**
Scatter plot between log_price and Overall score
```{r}
#TRANSFORMATION OF PRICE
fifa_data <- fifa_data %>% 
  mutate(log_price = log(Price))
```

```{r}
#Scatter plot after transformation
fifa_data %>% 
  filter(Price > 0) %>% 
  ggplot(data = .,aes(x = Overall, y= log_price))+
  geom_point()+
  geom_vline(xintercept = 65, color = "Red")+
  geom_smooth(method = "lm")
```
**Figure 3:**
Linear Regression summary statistics
```{r}
fifa_data %>% 
  filter(Price > 0) %>% 
  lm(formula = log_price ~ Overall, data = .) %>% 
  summary()
```
**Figure 4:** 
Scatter plot showing the average price per playing position
```{r, message=FALSE, warning=FALSE}
fifa_data %>% 
  group_by(Position) %>% 
  summarize(avg_price_position = mean(Price)) %>% 
  ggplot(data = ., aes(x = Position, y = avg_price_position))+
  geom_point()
```
**Figure 5:**
Outlier Identification
```{r}
fifa_data %>% 
  slice_max(Price, n=30) %>% 
  arrange(desc(Price))
```
**Figure 6:**
Analysis of relationship between salary and price of players
```{r}
fifa_data %>% 
  ggplot(data = ., aes(x = Price, y = Salary))+
  geom_point(alpha = 0.25)+
  geom_vline(xintercept = 30000000, color = "red")+
  geom_smooth(method = "lm")
```

# Analysis plan
##Player grouping
```{r}
fifa_data <- fifa_data %>% 
  mutate(Grouped_Position = ifelse(fifa_data$Position == 'RF', 'STRIKER',
                        ifelse(fifa_data$Position == 'CAM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'CB', 'DEFENCE',
                        ifelse(fifa_data$Position == 'CDM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'CF', 'STRIKER',
                        ifelse(fifa_data$Position == 'CM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'GK', 'GOAL-KEEPER',
                        ifelse(fifa_data$Position == 'LAM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'LB', 'DEFENCE',
                        ifelse(fifa_data$Position == 'LCB', 'DEFENCE',
                        ifelse(fifa_data$Position == 'LDM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'LCM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'LF', 'WING',
                        ifelse(fifa_data$Position == 'LM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'LW', 'WING',
                        ifelse(fifa_data$Position == 'LWB', 'DEFENCE', 
                        ifelse(fifa_data$Position == 'RAM', 'MIDFIELD', 
                        ifelse(fifa_data$Position == 'RB', 'DEFENCE', 
                        ifelse(fifa_data$Position == 'RCB', 'DEFENCE',
                        ifelse(fifa_data$Position == 'RCM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'RDM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'RF', 'STRIKER',
                        ifelse(fifa_data$Position == 'RM', 'MIDFIELD',
                        ifelse(fifa_data$Position == 'RS', 'STRIKER',
                        ifelse(fifa_data$Position == 'RW', 'WING',
                        ifelse(fifa_data$Position == 'RWB', 'DEFENCE',
                        ifelse(fifa_data$Position == 'ST', 'STRIKER',
                        ifelse(fifa_data$Position == 'LS', 'STRIKER',
                        'other')))))))))))))))))))))))))))))
```

```{r}
unique(fifa_data$Grouped_Position)
```

##Price analysis
```{r}
fifa_data %>% # Create a boxplot to understand the outliers (Superstars)
  ggplot(data = ., mapping = aes(x= log_price)) +
  geom_boxplot()
```
```{r}
#Calculate the range 
range(fifa_data$Price)
```

```{r}
#Calculate the Quantiles to see the the distribution of prices under the curve
quantile(fifa_data$Price)
```

```{r} 
# Filter data set by price, international reputation, and Potential to create the avg player data set
fifa_data_avg_player <- fifa_data %>% 
  filter(.data = ., Price < 9000000) 
```

```{r}
#check the range in prices of the avg players
range(fifa_data_avg_player$Price)
```

```{r}
#look at the distribution in prices of the avg player 
quantile(fifa_data_avg_player$Price) 
```

```{r}
fifa_superstars <- fifa_data %>% 
  filter(., Price > 9000000)  #number of superstar that we are not considering that are valued over $9,000,000 i.e SuperStars
```

```{r}
fifa_data_avg_player %>% 
  group_by(Grouped_Position) %>% 
  count() 
```
##Superstar Averages
```{r superstar averages, message=FALSE, warning=FALSE}
superstar_avg = fifa_superstars %>% 
  group_by(Grouped_Position) %>% 
  summarise(across(everything(), mean))
```


##Identifying stastically significant skills per position
**Playing position: STRIKER**
```{r}
#Finding out important attributes for Strikers players
fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "STRIKER") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .) %>% 
  summary()
```
**Playing position:WING**
```{r}
#Finding out important attributes for Wing players
fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "WING") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .) %>% 
  summary()
```
**Playing position:MIDFIELD**
```{r}
#filtering for MIDFIELD players
fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "MIDFIELD") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .) %>% 
  summary()

```
**Playing position:DEFENCE**
```{r}
#filtering for DEFENCE players
fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "DEFENCE") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .) %>% 
  summary()
```

**Playing position:GOAL-KEEPER**
```{r}
#filtering for GOAL-KEEPER players
fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "GOAL-KEEPER") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .) %>% 
  summary()
```

##Player Identification
###FILTERING FOR STRIKERS - 2 Main SKILLS
```{r}
fifa_data_avg_player %>%
  filter(Grouped_Position == "STRIKER") %>% 
  filter(Finishing >= 80.70 & HeadingAccuracy >= 73.98) %>% 
  arrange(desc(Price))
```


###FILTERING FOR WING - 2 Main SKILLS
```{r}
fifa_data_avg_player %>%
  filter(Grouped_Position == "WING") %>% 
  filter(SprintSpeed >= 82.84 & Crossing >= 75.82) %>% 
  arrange(desc(Price))
```

###Filtering for midfield - 2 main SKILLS
```{r}
fifa_data_avg_player %>% 
  filter(Grouped_Position == "MIDFIELD") %>%   
  filter(ShortPassing >= 79.67 & BallControl >= 80.84) %>% 
  arrange(desc(Price))
```

###FILTERING FOR DEFENCE - 2 Main SKILLS
```{r}
fifa_data_avg_player %>% 
  filter(Grouped_Position == "DEFENCE") %>%   
  filter(Strength >= 77.35 & SlidingTackle >= 79.7 & StandingTackle >= 81) %>% 
  arrange(desc(Price))
```
###FILTERING FOR Goal-keeper - 2 Main SKILLS
```{r}
fifa_data_avg_player %>% 
  filter(Grouped_Position == "GOAL-KEEPER") %>% 
  filter(GKDiving >= 82.34 & GKReflexes > 84.01 ) %>%
  arrange(desc(Price))
```

###Predicting Prices (Strikers) with Linear regression formula
```{r}
lm_strikers = fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "STRIKER") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .)
  

strikers_filtered = fifa_data_avg_player %>%
  filter(Grouped_Position == "STRIKER" & Price > 0)

predict_p_df = as_data_frame(broom::augment(lm_strikers))

strikers_predited <- cbind(strikers_filtered, predict_p_df$.fitted)

strikers_predited %>%
  filter(`predict_p_df$.fitted`< log_price)


```

###Predicting Prices (Wing) with Linear regression formula
```{r}
lm_wing = fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "WING") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .)
  

wing_filtered = fifa_data_avg_player %>%
  filter(Grouped_Position == "WING" & Price > 0)

predict_wing_df = as_data_frame(broom::augment(lm_wing))

wing_predited <- cbind(wing_filtered, predict_wing_df$.fitted)

wing_predited %>%
  filter(`predict_wing_df$.fitted`< log_price)
```

###Predicting Prices (MIDFIELD) with Linear regression formula
```{r}
lm_midfield = fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "MIDFIELD") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .)
  

midfield_filtered = fifa_data_avg_player %>%
  filter(Grouped_Position == "MIDFIELD" & Price > 0)

predict_midfield_df = as_data_frame(broom::augment(lm_midfield))

midfield_predited <- cbind(midfield_filtered, predict_midfield_df$.fitted)

midfield_predited %>%
  filter(`predict_midfield_df$.fitted`< log_price)
```

###Predicting Prices (DEFENCE) with Linear regression formula
```{r}
lm_defence = fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "DEFENCE") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .)
  

defence_filtered = fifa_data_avg_player %>%
  filter(Grouped_Position == "DEFENCE" & Price > 0)

predict_defence_df = as_data_frame(broom::augment(lm_defence))

defence_predited <- cbind(defence_filtered, predict_defence_df$.fitted)

defence_predited %>%
  filter(`predict_defence_df$.fitted`< log_price)
```

###Predicting Prices (GOAL-KEEPER) with Linear regression formula
```{r}
lm_goalie = fifa_data_avg_player %>% 
  filter(Price > 0 & Grouped_Position == "GOAL-KEEPER") %>%
  lm(formula = log_price ~ Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKReflexes, data = .)
  

goalie_filtered = fifa_data_avg_player %>%
  filter(Grouped_Position == "GOAL-KEEPER" & Price > 0)

predict_goalie_df = as_data_frame(broom::augment(lm_goalie))

goalie_predited <- cbind(goalie_filtered, predict_goalie_df$.fitted)

goalie_predited %>%
  filter(`predict_goalie_df$.fitted`< log_price)
```

#Output
Selecting Top Players for MIDFLIED Position, message=FALSE, warning=FALSE
```{r}
midfield_predited %>% 
  filter(ShortPassing >= 79.67 & BallControl >= 80.84 & `predict_midfield_df$.fitted`> log_price) %>% 
  arrange(desc(Price))
```

Selecting Top Players for GOAL - KEEPER Position, message=FALSE, warning=FALSE
```{r}
goalie_predited %>% 
  filter(GKDiving >= 82.34 & GKReflexes > 84.01  & `predict_goalie_df$.fitted`> log_price) %>% 
  arrange(desc(Price))
```

Selecting Top Players for DEFENCE Position, message=FALSE, warning=FALSE
```{r}
defence_predited %>% 
  filter(Strength >= 77.35 & SlidingTackle >= 79.7 & StandingTackle >= 81  & `predict_defence_df$.fitted`> log_price) %>% 
  arrange(desc(Price))
```

Selecting Top Players for WING Position, message=FALSE, warning=FALSE
```{r}
wing_predited %>% 
  filter(SprintSpeed >= 82.84 & Crossing >= 75.82  & `predict_wing_df$.fitted` > log_price) %>% 
  arrange(desc(Price))
```

Selecting Top Players for STRICKERS Position, message=FALSE, warning=FALSE
```{r}
strikers_predited %>% 
  filter(Finishing >= 80.70 & HeadingAccuracy >= 73.98  & `predict_p_df$.fitted`> log_price) %>% 
  arrange(desc(Price))
```